Book Image

Quantum Machine Learning and Optimisation in Finance

By : Antoine Jacquier, Oleksiy Kondratyev
Book Image

Quantum Machine Learning and Optimisation in Finance

By: Antoine Jacquier, Oleksiy Kondratyev

Overview of this book

With recent advances in quantum computing technology, we finally reached the era of Noisy Intermediate-Scale Quantum (NISQ) computing. NISQ-era quantum computers are powerful enough to test quantum computing algorithms and solve hard real-world problems faster than classical hardware. Speedup is so important in financial applications, ranging from analysing huge amounts of customer data to high frequency trading. This is where quantum computing can give you the edge. Quantum Machine Learning and Optimisation in Finance shows you how to create hybrid quantum-classical machine learning and optimisation models that can harness the power of NISQ hardware. This book will take you through the real-world productive applications of quantum computing. The book explores the main quantum computing algorithms implementable on existing NISQ devices and highlights a range of financial applications that can benefit from this new quantum computing paradigm. This book will help you be one of the first in the finance industry to use quantum machine learning models to solve classically hard real-world problems. We may have moved past the point of quantum computing supremacy, but our quest for establishing quantum computing advantage has just begun!
Table of Contents (4 chapters)

5
Quantum Boltzmann Machine

As we saw in Chapters 3 and 4, quantum annealing can be used to solve hard optimisation problems. However, the range of possible applications of quantum annealing is much wider than that. In this chapter, we will consider two distinct but related use cases that go beyond solving optimisation problems: sampling and training deep neural networks. Specifically, we will focus on the Quantum Boltzmann Machine (QBM) – a generative model that is a direct quantum annealing counterpart of the classical Restricted Boltzmann Machine (RBM), and the Deep Boltzmann Machine (DBM) – a class of deep neural networks composed of multiple layers of latent variables with connections between the layers but not between units within each layer.

We start by providing detailed descriptions of the classical RBM, including the corresponding training algorithm. Due to the fact that an RBM operates on stochastic binary activation units, one can establish the correspondence...